Abstract
In the current days, it is known that a great amount of effort is being applied to improving healthcare with the use of Artificial Intelligence technologies in order to assist healthcare professionals in the decision-making process. One of the most important field in healthcare diagnoses is the identification of Coronary Heart Disease since it has a high mortality rate worldwide. This disease occurs when the heart’s arteries are incapable of providing enough oxygen-rich blood to the heart. Thus, this study attempts to develop Data Mining models, using Machine Learning algorithms, capable of predicting, based on patients’ data, if a patient is at risk of developing any kind of Coronary Heart Disease within the next 10 years. To achieve this goal, the study was conducted by the CRISP-DM methodology and using the RapidMiner software. The best model was obtained using the Decision Tree algorithm and with Cross-Validation as the sampling method, obtaining an accuracy of 0.884, an AUC value of 0.942 and an F1-Score of 0.881.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Heart disease facts (2020). https://www.cdc.gov/heartdisease/facts.html
Ajmera, A.: Framingham heart study dataset (2021). https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset
Ettehad, D., et al.: Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis. The Lancet 387(10022), 957–967 (2016)
Grundy, S.M., et al.: 2018 aha/acc/aacvpr/aapa/abc/acpm/ada/ags/apha/aspc/nla/pcna guideline on the management of blood cholesterol: executive summary: a report of the American college of cardiology/American heart association task force on clinical practice guidelines. J. Am. Coll. Cardiol. 73(24), 3168–3209 (2019)
Guerrero, J., Segarra, M., Chorro, J., Bataller, M., Rosado, A., Espi, J.: Early effect of the suppression of the smoking habit on the heart rate variability. In: Computers in Cardiology, pp. 425–428. IEEE (2002)
Mozaffarian, D., et al.: Heart disease and stroke statistics-2015 update: a report from the American heart association. Circulation 131(4), e29–e322 (2015)
Neto, C., Brito, M., Lopes, V., Peixoto, H., Abelha, A., Machado, J.: Application of data mining for the prediction of mortality and occurrence of complications for gastric cancer patients. Entropy 21(12), 1163 (2019)
Neto, C., Peixoto, H., Abelha, V., Abelha, A., Machado, J.: Knowledge discovery from surgical waiting lists. Procedia Comput. Sci. 121, 1104–1111 (2017)
Nithya, B., Ilango, V.: Predictive analytics in health care using machine learning tools and techniques. In: 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 492–499. IEEE (2017)
Perk, J., et al.: European guidelines on cardiovascular disease prevention in clinical practice (version 2012). The fifth joint task force of the European society of cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of nine societies and by invited experts). Giornale italiano di cardiologia (2006) 14(5), 328–392 (2013)
Schäfer, F., Zeiselmair, C., Becker, J., Otten, H.: Synthesizing crisp-DM and quality management: a data mining approach for production processes. In: 2018 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD), pp. 190–195. IEEE (2018)
Shadabi, F., Sharma, D.: Artificial intelligence and data mining techniques in medicine-success stories. In: 2008 International Conference on BioMedical Engineering and Informatics, vol. 1, pp. 235–239. IEEE (2008)
Sharma, P., Singh, D., Singh, A.: Classification algorithms on a large continuous random dataset using rapid miner tool. In: 2015 2nd International Conference on Electronics and Communication Systems (ICECS), pp. 704–709. IEEE (2015)
Shashikant, R., Chetankumar, P.: Effect of obesity on heart rate variability among obese middle-aged individuals. In: 2019 International Conference on Advances in Computing, Communication and Control (ICAC3), pp. 1–5. IEEE (2019)
Wiles, R., Kinmonth, A.L.: Patients’ understandings of heart attack: implications for prevention of recurrence. Patient Educ. Counseling 44(2), 161–169 (2001)
Williams, B., et al.: 2018 esc/esh guidelines for the management of arterial hypertension: the task force for the management of arterial hypertension of the European society of cardiology (esc) and the European society of hypertension (esh). Eur. Heart J. 39(33), 3021–3104 (2018)
Acknowledgements
This work has been supported FCT—Fundação para a Ciência e Tecnologia (Portugal) within the Project Scope: UIDB /00319/2020.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Neto, C. et al. (2022). Prediction Models for Coronary Heart Disease. In: Matsui, K., Omatu, S., Yigitcanlar, T., González, S.R. (eds) Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference. DCAI 2021. Lecture Notes in Networks and Systems, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-030-86261-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-86261-9_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86260-2
Online ISBN: 978-3-030-86261-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)